Marcell Experimental Forest: S2 Stream Height & Air Temperature Comparison Analyses

Set Up

• Install/load necessary R packages
• Set working directory if necessary (or create a file path to use throughout the RMD to call the data from Box)

Read in water year data from CSVs in the intermediate_data directory

#read in necessary CSV files from prior RMDs
all_streamflow <- read_csv(here("intermediate_data", "all_streamflow.csv"))
air_temp_30_clean <- read_csv(here("intermediate_data", "air_temp_30_clean.csv"))
mc_clean <- read_csv(here("intermediate_data", "mc_clean.csv")) %>% 
  rename(datetime = collected)
precip_15_clean <- read_csv(here("intermediate_data", "precip_15_clean.csv")) 
precip_6h_clean <- read_csv(here("intermediate_data", "precip_6h_clean.csv")) 
precip_12h_clean <- read_csv(here("intermediate_data", "precip_12h_clean.csv")) 
precip_daily_clean <- read_csv(here("intermediate_data", "precip_daily_clean.csv")) 
precip_daily_join <- read_csv(here("intermediate_data", "precip_daily_join.csv")) 

Set constant values

#set constants 
min_year = 2017
max_year = 2021

Create sub-directories if necessary

output_dir <- file.path(here("intermediate_data"))

if (!dir.exists(output_dir)){
dir.create(output_dir)
} else {
    print("Directory already exists!")
}

## [1] "Directory already exists!"

output_dir <- file.path(here("figures"))

if (!dir.exists(output_dir)){
dir.create(output_dir)
} else {
    print("Directory already exists!")
}

## [1] "Directory already exists!"

Cumulative precipitation + air temperature

For interactive plots, cumulative precipitation is plotted as grey bars, and air temperature is plotted as a red line.

Cumulative precipitation (every 15min) + air temperature (every 30min)

#format precipitation data for plotting 
precip_15_join <- precip_15_clean %>% 
  pivot_wider(
    names_from = "precip_type",
    values_from = "precip_in"
  ) %>% 
  subset(year >= min_year & year <= max_year)

#format air temperature data for plotting 
airtemp_join <- air_temp_30_clean %>% 
  pivot_wider(
    names_from = "airtemp_type",
    values_from = "airtempC"
  ) %>% 
  subset(year >= min_year & year <= max_year) %>% 
  mutate(freezing = as.factor(freezing))

#format air temp data for plotting 
precip_air <- full_join(x = precip_15_join, 
                        y = airtemp_join, 
                        by = c("datetime", "year")) %>% 
  subset(year >= min_year & year <= max_year)

#plot 
p_15 <- ggplot() + 
  #airtemp data
  geom_line(data = airtemp_join, 
            aes(x = datetime, 
                y = airtempc_100),
            color = "pink",
            alpha = 0.5,
            size = 0.25) + 
  #precipitation data 
  geom_line(data = precip_15_join, 
            aes(x = datetime, 
                y = precip_10,
                colour = I("grey")),
            size = 0.25) +
  theme_classic() + 
  labs(x = "Time", 
       title = "S2 Precipitation Every 15min and Air Temperature Every 30min",
       subtitle = "Cumulative precipitation is plotted in grey. \n Air temperature is plotted in pink.") + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7)) + 
  scale_y_continuous(
    # Features of the first axis
    name = "Air Temperature (ºC) / 100",
    # Add a second axis and specify its features
    sec.axis = sec_axis(trans = ~., name = "Cumulative Precipitation (in) / 10")
  ) 

#static plot
p_15

#save figure
ggsave(filename = "precip_15_airtemp_30.png",
       plot = p_15,
       path = "figures/",
       width = 10,
       height = 5,
       units = c("in"),
       dpi = 300)

#interactive plot
#ggplotly(p_15)

Daily Cumulative Precipitation vs Air Temperature

#join air and precipitation data 
precip_air <- full_join(x = precip_15_join, 
                        y = airtemp_join, 
                        by = c("datetime", "year")) %>% 
  subset(year >= min_year & year <= max_year)

#plot
p_daily <- ggplot() + 
  #airtemp data
  geom_line(data = airtemp_join, 
            aes(x = datetime, 
                y = airtempc_100),
            color = "pink",
            alpha = 0.5,
            size = 0.25) + 
  #precipitation data 
  geom_line(data = precip_daily_join, 
            aes(x = datetime, 
                y = precip_10_in,
                colour = I("grey")),
            size = 0.25) +
  theme_classic() + 
  labs(x = "Time", 
       title = "S2 Daily Cumulative Precipitation and Air Temperature Every 30min",
       subtitle = "Daily cumulative precipitation is plotted in grey. \n Air temperature is plotted in pink.") + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7)) + 
  scale_y_continuous(
    # Features of the first axis
    name = "Air Temperature (ºC) / 100",
    # Add a second axis and specify its features
    sec.axis = sec_axis(trans = ~., name = "Cumulative Precipitation (in) / 10")
  ) 

#static plot
#p_daily

#save figure 
ggsave(filename = "precip_daily_airtemp_30.png",
       plot = p_daily,
       path = "figures/",
       width = 10,
       height = 5,
       units = c("in"),
       dpi = 300)

p_daily_interactive <- ggplot() + 
  #airtemp data
  geom_line(data = airtemp_join, 
            aes(x = datetime, 
                y = airtempc_100),
            color = "pink",
            alpha = 0.5,
            size = 0.25) + 
  #precipitation data 
  geom_line(data = precip_daily_join, 
            aes(x = datetime, 
                y = precip_10_in,
                colour = I("grey")),
            size = 0.25) +
  theme_classic() + 
  labs(x = "Time",
       y = "",
       title = "S2 Daily Cumulative Precipitation \n and Air Temperature Every 30min",
       subtitle = "Cumulative precipitation is plotted in grey. \n Air temperature is plotted in pink.") + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot
ggplotly(p_daily_interactive)

Streamflow + air temperature data (every 30min) + manual checks

#calculate the ratio of the 2 y-axes to plot the variables together
trans_value <- max(airtemp_join$airtempc_100, na.rm = TRUE) / max(all_streamflow$stream_height_ft, na.rm = TRUE)

#plot 
p_air <- ggplot() + 
  #airtemp data
  geom_line(data = airtemp_join, 
            aes(x = datetime,
                y = airtempc_100),
            color = "pink", 
            alpha = 0.4) +
  #streamflow data 
  geom_line(data = all_streamflow,
             aes(x = datetime, 
                 y = stream_height_ft),
            color = "blue",
            size = 0.75) + 
  #manual check points
  geom_point(data = mc_clean, 
             aes(x = datetime, 
                 y = stripchart_stage),
             size = 0.5) + 
  theme_classic() + 
  labs(x = "Time", 
       title = paste0("S2 Bog Streamflow and Air Temperature (", min_year, "-", max_year, ")"),
       subtitle = "Air temperature is plotted in pink. \n Streamflow is plotted in blue. \n Manual streamflow checkpoints are plotted as black dots.") + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7)) + 
  scale_y_continuous(
    # Features of the first axis
    name = "Air Temperature / 100 (ºC)",
    # Add a second axis and specify its features
    sec.axis = sec_axis(trans = ~.*trans_value, name = "Stream Height (ft)")
  ) 

#static plot
#p_air

#save the plot in the figures folder 
ggsave(filename = "streamflow_airtemp_mc.png",
       plot = p_air,
       path = "figures/",
       width = 8,
       height = 4,
       units = c("in"),
       dpi = 300)

#interactive plot
ggplotly(p_air)

Note that this file is being knit as index.html into the air_comparisons repository in order to update the GitHub pages website, where the plots can be viewed online.